Papers Known Unknowns
“Known Unknowns” 태그가 달린 논문 15편 · 필터 해제
Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules
Discovery of high-performance materials and molecules requires identifying extremes with property values that fall outside the known distribution. Therefore, the ability to extrapolate to out-of-distribution (OOD) proper…
Known UnknownsProperty PredictionResearchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents
Existing question answering (QA) datasets are no longer challenging to most powerful Large Language Models (LLMs). Traditional QA benchmarks like TriviaQA, NaturalQuestions, ELI5 and HotpotQA mainly study ``known unknown…
Known UnknownsQuestion AnsweringTriviaQAHigh-dimensional forecasting with known knowns and known unknowns
Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables fro…
Decision MakingDecision Making Under UncertaintyKnown UnknownsVariable SelectionThe known unknowns of the Hsp90 chaperone
Molecular chaperones are vital proteins that maintain protein homeostasis by assisting in protein folding, activation, degradation, and stress protection. Among them, heat-shock protein 90 (Hsp90) stands out as an essent…
Drug DesignKnown UnknownsProtein FoldingMachine learning for advancing low-temperature plasma modeling and simulation
Machine learning has had an enormous impact in many scientific disciplines. Also in the field of low-temperature plasma modeling and simulation it has attracted significant interest within the past years. Whereas its app…
Known UnknownsSurveyKnowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models
This paper investigates the capabilities of Large Language Models (LLMs) in the context of understanding their knowledge and uncertainty over questions. Specifically, we focus on addressing known-unknown questions, chara…
Known UnknownsOpen-Ended Question AnsweringQuestion AnsweringPaLM: Scaling Language Modeling with Pathways
Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed t…
Auto DebuggingCode GenerationCommon Sense ReasoningCoreference Resolution+19Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence …
AnachronismsAnalogical SimilarityAnalytic EntailmentCausal Judgment+69Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis o…
Abstract AlgebraAnachronismsAnalogical SimilarityAnalytic Entailment+143Domain Concretization from Examples: Addressing Missing Domain Knowledge via Robust Planning
The assumption of complete domain knowledge is not warranted for robot planning and decision-making in the real world. It could be due to design flaws or arise from domain ramifications or qualifications. In such cases, …
Decision MakingKnown UnknownsGenerative ODE Modeling with Known Unknowns
In several crucial applications, domain knowledge is encoded by a system of ordinary differential equations (ODE), often stemming from underlying physical and biological processes. A motivating example is intensive care …
Known UnknownsTime Series AnalysisThe division of labor in communication: Speakers help listeners account for asymmetries in visual perspective
Recent debates over adults' theory of mind use have been fueled by surprising failures of perspective-taking in communication, suggesting that perspective-taking can be relatively effortful. How, then, should speakers an…
Known UnknownsNavigateClassification Uncertainty of Deep Neural Networks Based on Gradient Information
We study the quantification of uncertainty of Convolutional Neural Networks (CNNs) based on gradient metrics. Unlike the classical softmax entropy, such metrics gather information from all layers of the CNN. We show for …
ClassificationGeneral ClassificationKnown UnknownsToward Open-Set Face Recognition
Much research has been conducted on both face identification and face verification, with greater focus on the latter. Research on face identification has mostly focused on using closed-set protocols, which assume that al…
Face IdentificationFace RecognitionFace VerificationKnown UnknownsKnown Unknowns: Uncertainty Quality in Bayesian Neural Networks
We evaluate the uncertainty quality in neural networks using anomaly detection. We extract uncertainty measures (e.g. entropy) from the predictions of candidate models, use those measures as features for an anomaly detec…
Anomaly DetectionKnown Unknowns